Complexity

Complexity in Manufacturing Processes and Systems


Status
Published

Lead Editor

1National University of Distance Education, Madrid, Spain

2Universidad de La Rioja, Logroño, Spain

3Autonomous University of Ciudad Juarez, Ciudad Juárez, Mexico

4Universidad del Valle, Cali, Colombia


Complexity in Manufacturing Processes and Systems

Description

Nowadays, manufacturing and systems processes are the field where various factors and variables interact, such as human resources, machines, tools, methodologies, and production techniques; all of them aimed to generate a product or service that can be delivered to customers with high quality and on time; the alteration of one of these systems’ components impacts on changes in another. That is, these components or subsystems are always related to each other in a complex way; to be able to make decisions efficiently, the understanding of these relationships and dependencies between the systems’ components should be sought as far as possible. Often, under the globalized production system, part of the production system is located in another country and context, which increases the number of variables and the complexity of the system.

However, to understand these relationships among the components of the manufacturing system, a process of obtaining reliable information for its analysis must be carried out and, fortunately, with the implementation of information and communication technologies, this work has been facilitated, since modern production processes currently have a high level of monitoring sensors. Nevertheless, such information must be integrated into methods and techniques that allow the decision-making process in a quick and efficient way, since a late decision may represent high costs due to products that do not meet the required quality specifications.

These complex manufacturing processes can be found from raw material supplying or procurement, the production process, and storage and distribution systems for finished products, among others, and it is now possible to find reports in the literature that are focused on understanding the relationships between these components of the manufacturing process. The techniques employed for the analysis of the information of these systems have been diverse, from simulation techniques and metahurisctics until causal techniques that associate variables, where the uncertainty has been integrated as vital as new variable.

Therefore, the main objective of this special issue is to collect and consolidate innovative and high-quality research contributions aimed to solve the complexity problem in production process and systems and the techniques and tools applied for a fast analysis and decision-making process. This special issue aims to provide insights on the recent advances in these topics by soliciting original scientific contributions in the form of theoretical foundations, models, experimental research, clinical studies for manufacturing process, and systems.

Potential topics include but are not limited to the following:

  • Industrial solutions through complexity theory
  • Modeling complexity of manufacturing systems
  • Performance of manufacturing complex systems: measurements and results
  • Mass customization as a complex system
  • Interactions between sustainability and complexity
  • Effects of new machine tools on complexity of processes
  • How the technology reduces the complexity
  • Techniques and tool used in complexity research
  • Algorithms for complex manufacturing systems

Articles

  • Special Issue
  • - Volume 2018
  • - Article ID 8738764
  • - Editorial

Complexity in Manufacturing Processes and Systems

Rosario Domingo | Julio Blanco-Fernández | ... | Leonardo Rivera
  • Special Issue
  • - Volume 2018
  • - Article ID 2602376
  • - Research Article

MOORA under Pythagorean Fuzzy Set for Multiple Criteria Decision Making

Luis Pérez-Domínguez | Luis Alberto Rodríguez-Picón | ... | Zeshui Xu
  • Special Issue
  • - Volume 2018
  • - Article ID 4084917
  • - Research Article

Reliability Assessment Methodology for Massive Manufacturing Using Multi-Function Equipment

M. López-Campos | F. Kristjanpoller | ... | R. Pascual
  • Special Issue
  • - Volume 2018
  • - Article ID 3418580
  • - Research Article

Optimizing a Biobjective Production-Distribution Planning Problem Using a GRASP

Martha-Selene Casas-Ramírez | José-Fernando Camacho-Vallejo | ... | José-Manuel Velarde-Cantú
  • Special Issue
  • - Volume 2018
  • - Article ID 4070496
  • - Research Article

Decision-Making Methodologies for Reuse of Industrial Assets

J. Claver | A. García-Domínguez | M. A. Sebastián
  • Special Issue
  • - Volume 2018
  • - Article ID 1254794
  • - Research Article

Modeling of Throughput in Production Lines Using Response Surface Methodology and Artificial Neural Networks

Federico Nuñez-Piña | Joselito Medina-Marin | ... | Eva Selene Hernandez-Gress
  • Special Issue
  • - Volume 2018
  • - Article ID 7374307
  • - Research Article

Measuring the Complex Construct of Macroergonomic Compatibility: A Manufacturing System Case Study

Arturo Realyvásquez | Aide A. Maldonado-Macías
  • Special Issue
  • - Volume 2018
  • - Article ID 9853635
  • - Research Article

Research on the Complexity and Chaos Control about a Closed-Loop Supply Chain with Dual-Channel Recycling and Uncertain Consumer Perception

Junhai Ma | Hao Ren | ... | Meihong Zhu
  • Special Issue
  • - Volume 2017
  • - Article ID 7317254
  • - Research Article

Coping with Complexity When Predicting Surface Roughness in Milling Processes: Hybrid Incremental Model with Optimal Parametrization

Gerardo Beruvides | Fernando Castaño | ... | Alberto Villalonga
  • Special Issue
  • - Volume 2017
  • - Article ID 4960106
  • - Research Article

Data-Driven Model-Free Adaptive Control of Particle Quality in Drug Development Phase of Spray Fluidized-Bed Granulation Process

Zhengsong Wang | Dakuo He | ... | Xu Wang
Complexity
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Acceptance rate11%
Submission to final decision120 days
Acceptance to publication21 days
CiteScore4.400
Journal Citation Indicator0.720
Impact Factor2.3
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